By Frédéric Holzmann
One of many subsequent demanding situations in vehicular know-how box is to enhance tremendously the line security. present advancements are concentrating on either automobile platform and various counsel platforms. This ebook provides a brand new engineering strategy in line with lean automobile structure prepared for the drive-by-wire technology.Based on a cognitive performance cut up, execution and command degrees are unique. The execution point centralized over the soundness keep an eye on plays the movement vector coming from the command point. At this point the motive force generates a movement vector that's regularly monitored by way of a digital co-pilot. the mixing of counsel platforms in a security correct multi-agent process is gifted the following to supply first an sufficient suggestions to the driving force to allow him get well a perilous scenario. strong concepts also are offered for the intervention part as soon as the command car should be optimized to stick in the security envelope.
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Extra resources for Adaptive Cooperation between Driver and Assistant System: Improving Road Safety
Therefore it is possible to estimate this friction coeﬃcient. Three diﬀerent approaches based on this idea are already under investigation as reported by 28 2 Road–tire µ friction coeﬃcient estimation Fig. 1. Ranges of µ Wang . Each approach focuses on a diﬀerent level of the vehicle-road model to extract some rules about the road-tire friction. A vehicle-based system  and a wheel-based system  determine the slip value and use a tire dynamic model to extract the µ-friction. The relation between the slip and µ is extracted from the Pacejka magic formula .
2 Predictive camera-based measurement 31 method requires more computation to be reliable for each type and size of cobblestones, for example. 3. The abscissa defines the possible levels of luminance (between 0 and 255) and the ordinate defines the number of pixels with such a luminance corresponding to a luminance bin. The snow can be easily detected because the luminance range of its histogram is not corresponding to the others as it is centered on the high luminance (eﬀect of the white color).
The driver estimates the µ-friction coeﬃcient through the recognition of the road surface condition and the quality of the contact road–tire. The measurement of the road surface includes sounds, vibrations, colors, shapes and surface gloss. Similarly to humans, every vehicle could use such inputs to deduce the µ-friction. The measurement of the road surface could be directly done with some external sensors such as camera, laser or microphone or with internal information such as the wheel rotation speed.